Pathways Language Model (PaLM)
美国
人工智能GPT-3替代大语模型(LLMS)

Pathways Language Model (PaLM) 翻译站点

扩展到突破性表现的5400亿参数

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今天的AI型号通常受过训练,只能做一件事。途径将使我们能够训练单个模型来做数千或数百万的事情。

当今的AI系统通常会从头开始训练每个新问题 - 数学模型的参数实际上是随机数的。想象一下,如果您每次学习新技能(例如跳绳),您都会忘记您学到的一切 - 如何平衡,如何跳跃,如何协调手的运动 - 并开始从没有什么。

这或多或少是我们今天训练大多数机器学习模型的方式。我们没有将现有模型扩展到学习新任务,而是从一无所有地训练每个新模型,只能做一件事情(或者有时我们将一般模型专门用于特定任务)。结果是,我们最终为成千上万个单独的任务开发了数千个模型。学习每个新任务的学习不仅需要更长的时间,而且还需要更多的数据来学习每个新任务,因为我们试图从无到有地学习有关世界和该任务的细节的所有内容(完全不同于人们如何处理新任务)。

取而代之的是,我们想培训一个不仅可以处理许多独立任务的模型,而且还可以利用并结合其现有技能,以更快,更有效地学习新任务。这样,模型通过对一项任务进行培训(例如,学习空中图像如何预测景观的高度)来学习的方式可以帮助它学习另一个任务 - 说,预测洪水将如何流过该地形。

我们希望模型具有不同的功能,可以根据需要调用,并将其缝合在一起以执行新的,更复杂的任务 - 更接近哺乳动物大脑在跨任务中概括的方式。

当今的模型主要集中在一种意义上。途径将带来多种感官。

资料来源:https://blog.google/technology/ai/introducing-pathways-nextways-next-generation-generation-ai-Architecture/

原文:

Today's AI models are typically trained to do only one thing. Pathways will enable us to train a single model to do thousands or millions of things.

Today’s AI systems are often trained from scratch for each new problem – the mathematical model’s parameters are initiated literally with random numbers. Imagine if, every time you learned a new skill (jumping rope, for example), you forgot everything you’d learned – how to balance, how to leap, how to coordinate the movement of your hands – and started learning each new skill from nothing.

That’s more or less how we train most machine learning models today. Rather than extending existing models to learn new tasks, we train each new model from nothing to do one thing and one thing only (or we sometimes specialize a general model to a specific task). The result is that we end up developing thousands of models for thousands of individual tasks. Not only does learning each new task take longer this way, but it also requires much more data to learn each new task, since we’re trying to learn everything about the world and the specifics of that task from nothing (completely unlike how people approach new tasks).

Instead, we’d like to train one model that can not only handle many separate tasks, but also draw upon and combine its existing skills to learn new tasks faster and more effectively. That way what a model learns by training on one task – say, learning how aerial images can predict the elevation of a landscape – could help it learn another task -- say, predicting how flood waters will flow through that terrain.

We want a model to have different capabilities that can be called upon as needed, and stitched together to perform new, more complex tasks – a bit closer to the way the mammalian brain generalizes across tasks.

Today's models mostly focus on one sense. Pathways will enable multiple senses.

Source: https://blog.google/technology/ai/introducing-pathways-next-generation-ai-architecture/

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